Question 553: The Observed Likelihood Uses Every Qualified Parameter Law

Original question and givens

Use complete theta labels=[a2, b2], observations=[x2, y2] and aligned exact probability rows=[[[3, 4], [1, 4]], [[1, 4], [3, 4]]]. Each theta row is nonnegative and sums to1. Sort labels with their associated rows/columns, never independently. Observed label=x2. Give (theta_values, observations, probability_rows, observed, likelihood_rows, certificate).

• G001 — Use complete theta labels=[a2, b2], observations=[x2, y2] and aligned exact probability rows=[[[3, 4], [1, 4]], [[1, 4], [3, 4]]]. Each theta row is nonnegative and sums to1. Sort labels with their associated rows/columns, never independently. Observed label=x2. Give (theta_values, observations, probability_rows, observed, likelihood_rows, certificate).

Definition and scope

Each parameter law is normalized. Fix the observation and compare its column; likelihood is not a posterior.

• mko-finite-maximum-likelihood-estimator

Step-by-step derivation

Θ=[a2, b2]; Ω=[x2, y2]

[[3/4, 1/4], [1/4, 3/4]]

P_a2(Ω) := sum(3/4, 1/4)

P_a2(Ω) = 1

P_b2(Ω) := sum(1/4, 3/4)

P_b2(Ω) = 1

parameter=a2; observed=x2

L(a2;x2)=3/4

parameter=b2; observed=x2

L(b2;x2)=1/4

[[a2, 3/4], [b2, 1/4]]

maximum=3/4; all argmax=[a2]

likelihood indexed by fixed observed outcome

not a posterior probability over parameters

Reverse checks

• S007 · premise_and_result_audit — Retain every parameter and sample label; distinguish null, empty tables, zero distributions and tied optima.
• S006 · premise_and_result_audit — Retain every parameter and sample label; distinguish null, empty tables, zero distributions and tied optima.
• S005 · premise_and_result_audit — Retain every parameter and sample label; distinguish null, empty tables, zero distributions and tied optima.
• S004 · premise_and_result_audit — Retain every parameter and sample label; distinguish null, empty tables, zero distributions and tied optima.
• S003 · premise_and_result_audit — Recompute P_b2(Ω) with exact rational arithmetic, not rounded decimals.
• S002 · premise_and_result_audit — Recompute P_a2(Ω) with exact rational arithmetic, not rounded decimals.
• S001 · premise_and_result_audit — Retain every parameter and sample label; distinguish null, empty tables, zero distributions and tied optima.

Result

([a2, b2], [x2, y2], [[3/4, 1/4], [1/4, 3/4]], x2, [[a2, 3/4], [b2, 1/4]], complete_normalized_finite_model_likelihood_table)

The full substitutions and premise audits recover the original answer, retaining its field order and verdict meanings.

This question review and source binding

Review date: 2026-10-26

Original questions, answers, step expressions, numeric ASTs and historical records are retained. Source candidate labels remain historical and are not overwritten as formal proof.